1. Technical Field
The invention is related to electronic networks. More particularly, the invention is related to radio access network load and condition aware traffic shaping control.
2. Description of the Background Art
Next-generation wireless networks are designed to provide quality-of-service (QoS) guarantees such as delay and data rate. Such designs support both deterministic and statistical guarantees. Operators have preferred statistical guarantees over the more severely conservative deterministic guarantees. Statistical guarantees support better revenue models for service offerings with limited and expensive wireless spectrum resources. A wireless scheduler with QoS guarantee differs from a wired scheduler because of the dependency of the former on the channel state of an end user. A wireless scheduler has to make use of asynchronous channel variations and multiuser diversity to implement QoS guarantees.
In addition to offering statistical QoS-assured services, operators also recognize the need for offering adaptive and best-effort services. Many web applications may not need strict statistical assurance. Some of these applications work reasonably well if their flow is adapted during QoS fluctuations and inter-cell hand-offs. Most of the other web applications perform well with best-effort services, bearing the burden of retrying lost transactions at the application level instead of relying on flow adaptation inside the operator network. A larger number of best-effort flows can be supported by resource limited wireless networks as compared to flows depending on network adaptation. The numbers of QoS-assured flows are even fewer than adaptive flows.
Wireless service providers have relied on provisioning their radio access network (RAN) resources and then relying on admission control policies to keep excess traffic off of the network. This has worked for circuit switched wireless phone services, and has also worked to a reasonable degree in the early phases of data wireless services on wireless networks.
Today, wireless service providers are realizing the limitations of reliance on admission control policies to keep out excess traffic load because such schemes more severely constrain revenue growth opportunity. Service providers often need premium-priced wireless subscription plans in addition to a larger number of non-premium subscribers. Such conflicting needs can be met only by attending to bandwidth hogging applications and keeping them from freely stealing resources from premium-priced traffic.
Rapid adoption of smart phones together with the growing numbers of web applications creates a need for an intelligent traffic control system to manage the unpredictable traffic mix. Such intelligence should reside outside the context of a standard wireless scheduler at the uU (or air) interface. An intelligent traffic control system identifies flows by the type of web applications to apply relative controls, taking into consideration such aspects as the current demands on RAN resources and the current fluctuations in RF links characteristics, such as cell loading and radio interference.
Priority based throttling is a concept that is well understood in wire line networks. Organizations usually need a way to let their high-priority traffic go ahead of their low-priority traffic when there is too much traffic entering their Wide Area Network (WAN) links. Layer Seven (L7) Rate Shaping techniques have been used for fine-grained bandwidth control to handle traffic spikes in wire line networks better. L7 techniques work well on wire line architectures, where the bandwidth to be shared among the competing traffic is steady. Solutions offered by L7 traffic shapers are applied on enterprise WAN access, CDNs and last mile Internet access points. They fall short of the needs in a next generation wireless access network.
Fine grained traffic control needs on a wireless access network are:
a) Recognizing the level of cell loading/congestion at a mobile user location;
b) Satisfying the bandwidth needs of higher priority and/or premium-priced application flow;
c) Lowering competition for bandwidth from lower priority and traffic; and
d) Letting low priority flow use bandwidth not needed by premium-priced flows.
It is also necessary to consider the operator's business decision policies. Operator policies may need to address such considerations as subscription plans, peak and non-peak hour traffic, subscriber preferences, characteristics and limitations of a mobile device.
Throttling or shaping traffic can reduce the negative impact of bandwidth-hungry applications, such as P2P, on RAN resources. These are essential, especially, during busy hours and in heavily congested cell sites. Following pre-defined policies and rules for Internet usage to limit certain type of traffic is necessary to implement differentiated service plans with different price points successfully. The effectiveness of pre-defined policies can be increased by including RAN awareness in policy triggers. For instance, premium-priced gaming services can experience smoother transit in congested cells through lowered competition from low priority traffic. Understanding what packets pertain to which data streams is critical for intelligent billing systems that are the backbone of customer-, service-, and application-based pricing packages. For instance, if customers buy and download a large video from a service provider's online shop, they might reasonably assume that the traffic is excluded from their fair usage allowance.
The standards (3GPP, 3GPP2) have specified four bearer classes of traffic, i.e. conversational, streaming, interactive, and background. There is a need to map finer level granularity IP side flows dynamically onto just these four classes on the RAN. There is a need to prioritize, block, and defer finer granularity flows either before they enter congested RAN backhaul links or busy cell sites. Therefore, it is desirable also to take these decisions based upon wireless user identity. In the case, for example, where:
(a) The system guarantees the associated QoS from the boundary of the RAN network out to the IP environment for a streaming application invoked from a mobile device; or
(b) The system guarantees the QoS from the service provider to the RAN boundary for push service.
The need to allow an operator to manage traffic growth and QoS in their network is well understood. 3GPP and other standards define the four main QoS classes discussed above as:
a) Conversational—voice/video;
b) Streaming—webinars/online classroom;
c) Interactive—browsing/file downloading; and
d) Background—email/DB updates.
Delay sensitivity is the main distinguishing characteristic of these QoS classes. However, application developers bury data deep inside packets, thus preventing simple classification and prioritization from being adequate. Today, operators must rely more on deep packet inspection (DPI) technology to classify and prioritize traffic. To keep up with the multiplying levels of data traffic, operators are also faced with the challenge of throttling bandwidth-hogging applications. Once a packet is classified, different policies are applied to the associated stream such as prioritization, rate limiting, and blocking. Operators deploy Policy Charging and Rules Functions (PCRF) to translate business decisions on to subscription plans.
Wireless access networks add a complexity dimension, namely RAN awareness, to decisions related to traffic shaping and QoS policies. For example, a more stringent policy may be necessary in a crowded RF cell than in a less crowded one. Similar policy differentiation could be applied based on a wireless base station profile. Base station profiles can, for example, can include fields to distinguish 2.5G, 3G, 4G RF links and backhaul bandwidth capacity limitations. Such profiles can enable an operator to enhance traffic flow management to throttle traffic from congesting limited RAN resources. For example, AT&T recently decided to block Slingbox traffic from iPhones on the AT&T 3G networks, indicating that this traffic would use large amounts of their 3G network's capacity. Also, rapid adoption of data cards, dongles, and netbooks is quickly opening the door for bandwidth-hogging peer-to-peer (P2P) traffic to migrate to wireless networks. Network operators will try to limit P2P traffic at least towards crowded cells to increase QoS for authentic subscriber traffic.
FIG. 1 is a schematic diagram that shows four base stations, each with different number of subscribers. Base station BS-A and BS-C are lightly crowded and probably located in less busy suburban location while base station BS-B is more likely located in a busy urban setting and is therefore most heavily crowded. Stringent throttling of bandwidth hogging applications may be called for in urban zones than in less busy suburban zones. In other words, a P2P application could operate without hindrance when operating in areas covered by BS-A and BS-C, while being required to be throttled when operating in areas covered by BS-B. Base station BS-D is moderately crowded and can operate better with some throttling of P2P applications, but to a lesser degree than in BS-B area. Crowding is likely to be less severe even in BS-B area during non-busy hours, thereby relaxing the need for throttling during those periods.
Thus, it would be advantageous to enable an operator to support a mission critical application competing for scarce RF resources in a crowded cell in the midst of long video downloads and bandwidth stealing P2P applications